{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPUs, 2 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets/\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.9345 - acc: 0.6071 - val_loss: 1.1407 - val_acc: 0.7640 - time: 2.1074\n",
      "Testing...\n",
      "2/2 [==============================] - 0s 110ms/step - test_loss: 1.4947 - test_acc: 0.7990 - time: 0.2193\n",
      "Test loss 1.4947, Test accuracy 79.90%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import GraphSAGE\n",
    "model = GraphSAGE(graph, n_samples=(15,5), attr_transform=\"normalize_attr\", device='GPU', seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=100)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"graph_sage\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attr_matrix (InputLayer)        [(None, 1433)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "neighbors_0 (InputLayer)        [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "neighbors_1 (InputLayer)        [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "nodes (InputLayer)              [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_embedding_lookup_1  [(None, 1433)]       0           attr_matrix[0][0]                \n",
      "                                                                 neighbors_0[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_embedding_lookup_2  [(None, 1433)]       0           attr_matrix[0][0]                \n",
      "                                                                 neighbors_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_embedding_lookup (T [(None, 1433)]       0           attr_matrix[0][0]                \n",
      "                                                                 nodes[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_embedding_lookup_1/ [(None, 1433)]       0           tf_op_layer_embedding_lookup_1[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_embedding_lookup_2/ [(None, 1433)]       0           tf_op_layer_embedding_lookup_2[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_embedding_lookup/Id [(None, 1433)]       0           tf_op_layer_embedding_lookup[0][0\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Reshape (TensorFlow [(None, 15, 1433)]   0           tf_op_layer_embedding_lookup_1/Id\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Reshape_1 (TensorFl [(None, 5, 1433)]    0           tf_op_layer_embedding_lookup_2/Id\n",
      "__________________________________________________________________________________________________\n",
      "mean_aggregator (MeanAggregator (None, 64)           91776       tf_op_layer_embedding_lookup/Iden\n",
      "                                                                 tf_op_layer_Reshape[0][0]        \n",
      "                                                                 tf_op_layer_embedding_lookup_1/Id\n",
      "                                                                 tf_op_layer_Reshape_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 64)           0           mean_aggregator[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Reshape_2 (TensorFl [(None, 15, 64)]     0           mean_aggregator[1][0]            \n",
      "__________________________________________________________________________________________________\n",
      "mean_aggregator_1 (MeanAggregat (None, 7)            903         dropout[0][0]                    \n",
      "                                                                 tf_op_layer_Reshape_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 7)            0           mean_aggregator_1[0][0]          \n",
      "==================================================================================================\n",
      "Total params: 92,679\n",
      "Trainable params: 92,679\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
